Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images

Document type: Conference Papers
Peer reviewed: Yes
Full text:
Author(s): Azam Sheikh Muhammad, Niklas Lavesson, Paul Davidsson, Mikael Nilsson
Title: Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images
Conference name: 13th International Conference on Computer Analysis of Images and Patterns Munster, GERMANY, SEP 02-04, 2009
Year: 2009
Pagination: 1220-1227
ISBN: 978-3-642-03766-5
Publisher: Springer
City: Munster
ISI number: 000273458100148
Organization: Blekinge Institute of Technology
Department: School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
Authors e-mail:,,,
Language: English
Abstract: Traffic Sign Recognition is a widely studied problem and its dynamic nature calls for the application of a broad range of preprocessing, segmentation, and recognition techniques but few databases are available for evaluation. We have produced a database consisting of 1,300 images captured by a video camera. On this database we have conducted a systematic experimental study. We used four different preprocessing techniques and designed a generic speed sign segmentation algorithm. Then we selected a range of contemporary speed sign classification algorithms using shape based segmented binary images for training and evaluated their results using four metrics, including accuracy and processing speed. The results indicate that Naive Bayes and Random Forest seem particularly well suited for this recognition task. Moreover, we show that two specific preprocessing techniques appear to provide a better basis for concept learning than the others.
Subject: Computer Science\Artificial Intelligence
Keywords: road sign, classification, supervised learning
Note: Source: COMPUTER ANALYSIS OF IMAGES AND PATTERNS, PROCEEDINGS Book Series: Lecture Notes in Computer Science Volume: 5702 Pages: 1220-1227 Published: 2009